{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkContext\n",
    "from pyspark import SparkConf\n",
    "#sc = SparkContext()\n",
    "import pyspark\n",
    "from pyspark import SparkConf, SparkContext \n",
    "import pandas as pd \n",
    "from pyspark.ml.feature import StringIndexer\n",
    "from pyspark.sql.functions import *\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark_conf = SparkConf().set('spark.executor.memory', '6G').set('spark.driver.memory', '6G').set('spark.driver.maxResultSize', '6G')\n",
    "spark_conf = SparkConf().setAppName(\"test\")\n",
    "sc = SparkContext(conf = spark_conf)\n",
    "# sc=SparkContext.getOrCreate()\n",
    "from pyspark.sql import SQLContext\n",
    "sqlContext = SQLContext(sc)\n",
    "from pyspark.sql.session import SparkSession\n",
    "spark = SparkSession(sc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read traffic weather merged data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_data = spark.read.parquet('/Users/pprusty05/workspace/Data_mining/Project/data_folder/traffic_weather_data/TrafficWeatherMerged.parquet')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- TR_EventId: string (nullable = true)\n",
      " |-- TR_Type: string (nullable = true)\n",
      " |-- TR_Severity: string (nullable = true)\n",
      " |-- TR_TMC: integer (nullable = true)\n",
      " |-- TR_StartTime: timestamp (nullable = true)\n",
      " |-- TR_EndTime: timestamp (nullable = true)\n",
      " |-- TR_LocationLat: double (nullable = true)\n",
      " |-- TR_LocationLng: double (nullable = true)\n",
      " |-- TR_Distance: double (nullable = true)\n",
      " |-- TR_AirportCode: string (nullable = true)\n",
      " |-- TR_Street: string (nullable = true)\n",
      " |-- TR_Side: string (nullable = true)\n",
      " |-- TR_City: string (nullable = true)\n",
      " |-- TR_County: string (nullable = true)\n",
      " |-- TR_State: string (nullable = true)\n",
      " |-- TR_ZipCode: integer (nullable = true)\n",
      " |-- TR_StartDate: date (nullable = true)\n",
      " |-- TR_EndDate: date (nullable = true)\n",
      " |-- WR_EventId: string (nullable = true)\n",
      " |-- WR_Type: string (nullable = true)\n",
      " |-- WR_Severity: string (nullable = true)\n",
      " |-- WR_StartTime: timestamp (nullable = true)\n",
      " |-- WR_EndTime: timestamp (nullable = true)\n",
      " |-- WR_TimeZone: string (nullable = true)\n",
      " |-- WR_LocationLat: double (nullable = true)\n",
      " |-- WR_LocationLng: double (nullable = true)\n",
      " |-- WR_AirportCode: string (nullable = true)\n",
      " |-- WR_City: string (nullable = true)\n",
      " |-- WR_County: string (nullable = true)\n",
      " |-- WR_State: string (nullable = true)\n",
      " |-- WR_ZipCode: integer (nullable = true)\n",
      " |-- WR_StartDate: date (nullable = true)\n",
      " |-- WR_EndDate: date (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_merged_data.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5546343"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326850"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_data[df_merged_data.TR_Type == 'Accident'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_missing_values(df):\n",
    "    missing_value_dict = {}\n",
    "    for column_name in df.columns:\n",
    "        missing_value_count = df.where(df[column_name].isNull()).count()\n",
    "        missing_value_percent = (missing_value_count / df.count()) * 100\n",
    "        missing_value_dict[column_name] = {}\n",
    "        missing_value_dict[column_name]['count'] =  missing_value_count\n",
    "        missing_value_dict[column_name]['percent'] =  missing_value_percent\n",
    "\n",
    "    missing_value_pd = pd.DataFrame(missing_value_dict).T\n",
    "    return missing_value_pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>TR_EventId</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_Type</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_Severity</td>\n",
       "      <td>712913.0</td>\n",
       "      <td>12.853749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_TMC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_StartTime</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_EndTime</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_LocationLat</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_LocationLng</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_Distance</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_AirportCode</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_Street</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_Side</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_County</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_State</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_ZipCode</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_StartDate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_EndDate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_EventId</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_Type</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_Severity</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_StartTime</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_EndTime</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_TimeZone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_LocationLat</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_LocationLng</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_AirportCode</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_County</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_State</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_ZipCode</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_StartDate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_EndDate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count    percent\n",
       "TR_EventId           0.0   0.000000\n",
       "TR_Type              0.0   0.000000\n",
       "TR_Severity     712913.0  12.853749\n",
       "TR_TMC               0.0   0.000000\n",
       "TR_StartTime         0.0   0.000000\n",
       "TR_EndTime           0.0   0.000000\n",
       "TR_LocationLat       0.0   0.000000\n",
       "TR_LocationLng       0.0   0.000000\n",
       "TR_Distance          0.0   0.000000\n",
       "TR_AirportCode       0.0   0.000000\n",
       "TR_Street            0.0   0.000000\n",
       "TR_Side              0.0   0.000000\n",
       "TR_City              0.0   0.000000\n",
       "TR_County            0.0   0.000000\n",
       "TR_State             0.0   0.000000\n",
       "TR_ZipCode           0.0   0.000000\n",
       "TR_StartDate         0.0   0.000000\n",
       "TR_EndDate           0.0   0.000000\n",
       "WR_EventId           0.0   0.000000\n",
       "WR_Type              0.0   0.000000\n",
       "WR_Severity          0.0   0.000000\n",
       "WR_StartTime         0.0   0.000000\n",
       "WR_EndTime           0.0   0.000000\n",
       "WR_TimeZone          0.0   0.000000\n",
       "WR_LocationLat       0.0   0.000000\n",
       "WR_LocationLng       0.0   0.000000\n",
       "WR_AirportCode       0.0   0.000000\n",
       "WR_City              0.0   0.000000\n",
       "WR_County            0.0   0.000000\n",
       "WR_State             0.0   0.000000\n",
       "WR_ZipCode           0.0   0.000000\n",
       "WR_StartDate         0.0   0.000000\n",
       "WR_EndDate           0.0   0.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_missing_values(df_merged_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+-------+\n",
      "|       TR_Type|  count|\n",
      "+--------------+-------+\n",
      "|         Event|   4944|\n",
      "|Broken-Vehicle|  62853|\n",
      "|  Lane-Blocked|  51661|\n",
      "|  Construction|  72974|\n",
      "|    Congestion|4822216|\n",
      "| Flow-Incident| 204845|\n",
      "|      Accident| 326850|\n",
      "+--------------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_merged_data.groupby('TR_Type').count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-------+\n",
      "|      WR_Type|  count|\n",
      "+-------------+-------+\n",
      "|         Cold| 107622|\n",
      "|          Fog|1225215|\n",
      "|        Storm|  39026|\n",
      "|Precipitation|  76617|\n",
      "|         Hail|   5589|\n",
      "|         Rain|2529071|\n",
      "|         Snow|1563203|\n",
      "+-------------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_merged_data.groupby('WR_Type').count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+\n",
      "|WR_Severity|\n",
      "+-----------+\n",
      "|      Light|\n",
      "|   Moderate|\n",
      "|      Light|\n",
      "|     Severe|\n",
      "+-----------+\n",
      "only showing top 4 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_merged_data.select('WR_Severity').show(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_data.columns\n",
    "drop_columns_list = ['TR_EventId', 'TR_TMC', 'TR_LocationLat', 'TR_LocationLng', 'TR_Distance', 'TR_AirportCode',\n",
    "                    'TR_Side', 'TR_County','TR_StartDate','TR_EndDate','TR_EndTime', 'WR_EventId', 'WR_StartTime', 'WR_EndTime', 'WR_TimeZone',\n",
    "                    'WR_LocationLat', 'WR_LocationLng','WR_AirportCode','WR_City','WR_County', 'WR_State', 'WR_ZipCode',\n",
    "                    'WR_StartDate','WR_EndDate','TR_Severity']\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_truc_cols = df_merged_data\n",
    "for col_names in drop_columns_list:\n",
    "    df_truc_cols = df_truc_cols.drop(col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- TR_Type: string (nullable = true)\n",
      " |-- TR_StartTime: timestamp (nullable = true)\n",
      " |-- TR_Street: string (nullable = true)\n",
      " |-- TR_City: string (nullable = true)\n",
      " |-- TR_State: string (nullable = true)\n",
      " |-- TR_ZipCode: integer (nullable = true)\n",
      " |-- WR_Type: string (nullable = true)\n",
      " |-- WR_Severity: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_truc_cols.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_truc_cols = df_truc_cols.filter(df_truc_cols.TR_Severity.isNotNull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+-------+\n",
      "|       TR_Type|  count|\n",
      "+--------------+-------+\n",
      "|         Event|   4944|\n",
      "|Broken-Vehicle|  62853|\n",
      "|  Lane-Blocked|  51661|\n",
      "|  Construction|  72974|\n",
      "|    Congestion|4822216|\n",
      "| Flow-Incident| 204845|\n",
      "|      Accident| 326850|\n",
      "+--------------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_truc_cols.groupby('TR_Type').count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>TR_Type</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_StartTime</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_Street</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_State</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TR_ZipCode</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_Type</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WR_Severity</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count  percent\n",
       "TR_Type         0.0      0.0\n",
       "TR_StartTime    0.0      0.0\n",
       "TR_Street       0.0      0.0\n",
       "TR_City         0.0      0.0\n",
       "TR_State        0.0      0.0\n",
       "TR_ZipCode      0.0      0.0\n",
       "WR_Type         0.0      0.0\n",
       "WR_Severity     0.0      0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_missing_values(df_truc_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_truc_cols.write.parquet('/Users/pprusty05/workspace/Data_mining/Project/data_folder/traffic_weather_data/TW_NonNull.parquet')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read non null data with ralivant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_truc_cols=spark.read.parquet('/Users/pprusty05/workspace/Data_mining/Project/data_folder/traffic_weather_data/TW_NonNull.parquet')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make an accident column =1 if accident 0 otherwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import functions as F\n",
    "df_accident=df_truc_cols.withColumn('Accident', F.when((F.col(\"TR_Type\") == 'Accident') , 1) \\\n",
    "    .otherwise(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-------+\n",
      "|Accident|  count|\n",
      "+--------+-------+\n",
      "|       1| 326850|\n",
      "|       0|5219493|\n",
      "+--------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_accident.groupby('Accident').count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_accident = df_accident.drop('TR_Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- TR_StartTime: timestamp (nullable = true)\n",
      " |-- TR_Street: string (nullable = true)\n",
      " |-- TR_City: string (nullable = true)\n",
      " |-- TR_State: string (nullable = true)\n",
      " |-- TR_ZipCode: integer (nullable = true)\n",
      " |-- WR_Type: string (nullable = true)\n",
      " |-- WR_Severity: string (nullable = true)\n",
      " |-- Accident: integer (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_accident.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make hour column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_accident = df_accident.withColumn('Hour', hour(df_accident.TR_StartTime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+\n",
      "|Hour|\n",
      "+----+\n",
      "|  20|\n",
      "|  20|\n",
      "|  20|\n",
      "|  20|\n",
      "|  21|\n",
      "|  21|\n",
      "|  21|\n",
      "|  21|\n",
      "|  21|\n",
      "|  21|\n",
      "+----+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_accident.select('Hour').show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# downsample to remove skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_acc_downsample=df_accident.sampleBy('Accident', fractions={0: 326850./5219493, 1: 1.0}).cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+------+\n",
      "|Accident| count|\n",
      "+--------+------+\n",
      "|       1|326850|\n",
      "|       0|326642|\n",
      "+--------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_acc_downsample.groupby('Accident').count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_acc_downsample.write.parquet('/Users/pprusty05/workspace/Data_mining/Project/data_folder/traffic_weather_data/Accident_downsampled.parquet')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_acc_downsample=spark.read.parquet('/Users/pprusty05/workspace/Data_mining/Project/data_folder/traffic_weather_data/Accident_downsampled.parquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_data_types(df):\n",
    "#     col_data_type = {}\n",
    "#     for types in df.dtypes:\n",
    "#         col_data_type[types[0]] = types[1]\n",
    "#     return col_data_type\n",
    "\n",
    "\n",
    "# col_data_type = get_data_types(df_acc_downsample)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# convert to panda df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "accident_panda_df=df_acc_downsample.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TR_StartTime</th>\n",
       "      <th>TR_Street</th>\n",
       "      <th>TR_City</th>\n",
       "      <th>TR_State</th>\n",
       "      <th>TR_ZipCode</th>\n",
       "      <th>WR_Type</th>\n",
       "      <th>WR_Severity</th>\n",
       "      <th>Accident</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-09-27 20:08:00</td>\n",
       "      <td>E Main St</td>\n",
       "      <td>Westfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1085</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-09-27 20:08:00</td>\n",
       "      <td>E Main St</td>\n",
       "      <td>Westfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1085</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2019-12-10 13:40:00</td>\n",
       "      <td>East St</td>\n",
       "      <td>Pittsfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1201</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019-12-10 20:05:00</td>\n",
       "      <td>East St</td>\n",
       "      <td>Pittsfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1201</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019-12-10 21:54:00</td>\n",
       "      <td>South St</td>\n",
       "      <td>Pittsfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1201</td>\n",
       "      <td>Snow</td>\n",
       "      <td>Heavy</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         TR_StartTime  TR_Street     TR_City TR_State  TR_ZipCode WR_Type  \\\n",
       "0 2016-09-27 20:08:00  E Main St   Westfield       MA        1085    Rain   \n",
       "1 2016-09-27 20:08:00  E Main St   Westfield       MA        1085    Rain   \n",
       "2 2019-12-10 13:40:00    East St  Pittsfield       MA        1201    Rain   \n",
       "3 2019-12-10 20:05:00    East St  Pittsfield       MA        1201    Rain   \n",
       "4 2019-12-10 21:54:00   South St  Pittsfield       MA        1201    Snow   \n",
       "\n",
       "  WR_Severity  Accident  Hour  \n",
       "0       Light         0    20  \n",
       "1    Moderate         0    20  \n",
       "2       Light         0    13  \n",
       "3       Light         0    20  \n",
       "4       Heavy         0    21  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_panda_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## drop starttime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "accident_panda_df=accident_panda_df.drop(['TR_StartTime'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TR_Street</th>\n",
       "      <th>TR_City</th>\n",
       "      <th>TR_State</th>\n",
       "      <th>TR_ZipCode</th>\n",
       "      <th>WR_Type</th>\n",
       "      <th>WR_Severity</th>\n",
       "      <th>Accident</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>E Main St</td>\n",
       "      <td>Westfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1085</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>E Main St</td>\n",
       "      <td>Westfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1085</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>East St</td>\n",
       "      <td>Pittsfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1201</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>East St</td>\n",
       "      <td>Pittsfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1201</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South St</td>\n",
       "      <td>Pittsfield</td>\n",
       "      <td>MA</td>\n",
       "      <td>1201</td>\n",
       "      <td>Snow</td>\n",
       "      <td>Heavy</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TR_Street     TR_City TR_State  TR_ZipCode WR_Type WR_Severity  Accident  \\\n",
       "0  E Main St   Westfield       MA        1085    Rain       Light         0   \n",
       "1  E Main St   Westfield       MA        1085    Rain    Moderate         0   \n",
       "2    East St  Pittsfield       MA        1201    Rain       Light         0   \n",
       "3    East St  Pittsfield       MA        1201    Rain       Light         0   \n",
       "4   South St  Pittsfield       MA        1201    Snow       Heavy         0   \n",
       "\n",
       "   Hour  \n",
       "0    20  \n",
       "1    20  \n",
       "2    13  \n",
       "3    20  \n",
       "4    21  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_panda_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24540"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(accident_panda_df['TR_Street'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1726"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(accident_panda_df['TR_City'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(accident_panda_df['TR_State'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(accident_panda_df['WR_Type'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(accident_panda_df['WR_Severity'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1908"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(accident_panda_df['TR_ZipCode'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TR_Street', 'TR_City', 'TR_State', 'WR_Type', 'WR_Severity'], dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_panda_df.select_dtypes(include=['object', 'category']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TR_Street', 'TR_City', 'TR_State', 'TR_ZipCode', 'WR_Type',\n",
       "       'WR_Severity', 'Accident', 'Hour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_panda_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# convert catagorical to numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "df_lable_encoded = accident_panda_df[['TR_Street', 'TR_City', 'TR_State', 'TR_ZipCode']].apply(le.fit_transform)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>19696</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TR_Street  TR_City  TR_State  TR_ZipCode\n",
       "0       5791     1670        17           0\n",
       "1       5791     1670        17           0\n",
       "2       6423     1229        17           1\n",
       "3       6423     1229        17           1\n",
       "4      19696     1229        17           1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lable_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "accident_panda_df['TR_Street'] = le.fit_transform(accident_panda_df['TR_Street'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "accident_panda_df['TR_City'] = le.fit_transform(accident_panda_df['TR_City'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "accident_panda_df['TR_State'] = le.fit_transform(accident_panda_df['TR_State'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "accident_panda_df['TR_ZipCode'] = le.fit_transform(accident_panda_df['TR_ZipCode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TR_Street</th>\n",
       "      <th>TR_City</th>\n",
       "      <th>TR_State</th>\n",
       "      <th>TR_ZipCode</th>\n",
       "      <th>WR_Type</th>\n",
       "      <th>WR_Severity</th>\n",
       "      <th>Accident</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>Rain</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6423</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
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       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
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       "      <td>3</td>\n",
       "      <td>6423</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>19696</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Snow</td>\n",
       "      <td>Heavy</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>14923</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>14923</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Snow</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3722</td>\n",
       "      <td>309</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Heavy</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8540</td>\n",
       "      <td>309</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3722</td>\n",
       "      <td>309</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TR_Street  TR_City  TR_State  TR_ZipCode WR_Type WR_Severity  Accident  \\\n",
       "0       5791     1670        17           0    Rain       Light         0   \n",
       "1       5791     1670        17           0    Rain    Moderate         0   \n",
       "2       6423     1229        17           1    Rain       Light         0   \n",
       "3       6423     1229        17           1    Rain       Light         0   \n",
       "4      19696     1229        17           1    Snow       Heavy         0   \n",
       "5      14923     1229        17           1    Rain       Light         0   \n",
       "6      14923     1229        17           1    Snow       Light         0   \n",
       "7       3722      309        17           6    Rain       Heavy         0   \n",
       "8       8540      309        17           6    Rain       Light         0   \n",
       "9       3722      309        17           6    Rain    Moderate         0   \n",
       "\n",
       "   Hour  \n",
       "0    20  \n",
       "1    20  \n",
       "2    13  \n",
       "3    20  \n",
       "4    21  \n",
       "5    22  \n",
       "6    22  \n",
       "7    10  \n",
       "8    11  \n",
       "9    11  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_panda_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dummies = pd.get_dummies(accident_panda_df, columns=['WR_Severity','WR_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>TR_Street</th>\n",
       "      <th>TR_City</th>\n",
       "      <th>TR_State</th>\n",
       "      <th>TR_ZipCode</th>\n",
       "      <th>Accident</th>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>6423</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>19696</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   TR_Street  TR_City  TR_State  TR_ZipCode  Accident  Hour  \\\n",
       "0       5791     1670        17           0         0    20   \n",
       "1       5791     1670        17           0         0    20   \n",
       "2       6423     1229        17           1         0    13   \n",
       "3       6423     1229        17           1         0    20   \n",
       "4      19696     1229        17           1         0    21   \n",
       "\n",
       "   WR_Severity_Heavy  WR_Severity_Light  WR_Severity_Moderate  \\\n",
       "0                  0                  1                     0   \n",
       "1                  0                  0                     1   \n",
       "2                  0                  1                     0   \n",
       "3                  0                  1                     0   \n",
       "4                  1                  0                     0   \n",
       "\n",
       "   WR_Severity_Other  WR_Severity_Severe  WR_Severity_UNK  WR_Type_Cold  \\\n",
       "0                  0                   0                0             0   \n",
       "1                  0                   0                0             0   \n",
       "2                  0                   0                0             0   \n",
       "3                  0                   0                0             0   \n",
       "4                  0                   0                0             0   \n",
       "\n",
       "   WR_Type_Fog  WR_Type_Hail  WR_Type_Precipitation  WR_Type_Rain  \\\n",
       "0            0             0                      0             1   \n",
       "1            0             0                      0             1   \n",
       "2            0             0                      0             1   \n",
       "3            0             0                      0             1   \n",
       "4            0             0                      0             0   \n",
       "\n",
       "   WR_Type_Snow  WR_Type_Storm  \n",
       "0             0              0  \n",
       "1             0              0  \n",
       "2             0              0  \n",
       "3             0              0  \n",
       "4             1              0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.preprocessing import StandardScaler "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# target =accident"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df_dummies['Accident']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Accident, dtype: int32"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_dummies.drop(['Accident'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>6423</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>19696</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TR_Street  TR_City  TR_State  TR_ZipCode  Hour  WR_Severity_Heavy  \\\n",
       "0       5791     1670        17           0    20                  0   \n",
       "1       5791     1670        17           0    20                  0   \n",
       "2       6423     1229        17           1    13                  0   \n",
       "3       6423     1229        17           1    20                  0   \n",
       "4      19696     1229        17           1    21                  1   \n",
       "\n",
       "   WR_Severity_Light  WR_Severity_Moderate  WR_Severity_Other  \\\n",
       "0                  1                     0                  0   \n",
       "1                  0                     1                  0   \n",
       "2                  1                     0                  0   \n",
       "3                  1                     0                  0   \n",
       "4                  0                     0                  0   \n",
       "\n",
       "   WR_Severity_Severe  WR_Severity_UNK  WR_Type_Cold  WR_Type_Fog  \\\n",
       "0                   0                0             0            0   \n",
       "1                   0                0             0            0   \n",
       "2                   0                0             0            0   \n",
       "3                   0                0             0            0   \n",
       "4                   0                0             0            0   \n",
       "\n",
       "   WR_Type_Hail  WR_Type_Precipitation  WR_Type_Rain  WR_Type_Snow  \\\n",
       "0             0                      0             1             0   \n",
       "1             0                      0             1             0   \n",
       "2             0                      0             1             0   \n",
       "3             0                      0             1             0   \n",
       "4             0                      0             0             1   \n",
       "\n",
       "   WR_Type_Storm  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scalar = scaler.fit_transform(X_train)\n",
    "X_test_scalar=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "y_pred = gnb.fit(X_train, y_train).predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6207719674075207"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6612725769306992\n",
      "0.5965771075572435\n",
      "0.7417065244051999\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test,y_pred))\n",
    "print(precision_score(y_test,y_pred))\n",
    "print(recall_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(X_train, y_train)\n",
    "knn_pred=neigh.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8675873149458705\n",
      "0.8716754776857544\n",
      "0.8441058632623462\n",
      "0.9011068187392691\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,knn_pred))\n",
    "print(f1_score(y_test,knn_pred))\n",
    "print(precision_score(y_test,knn_pred))\n",
    "print(recall_score(y_test,knn_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_range=range(1,20)\n",
    "score={}\n",
    "score_list=[]\n",
    "for k in k_range:\n",
    "    neigh = KNeighborsClassifier(n_neighbors=k)\n",
    "    neigh.fit(X_train, y_train)\n",
    "    knn_pred=neigh.predict(X_test)\n",
    "    score[k]=accuracy_score(y_test,knn_pred)\n",
    "    score_list.append(accuracy_score(y_test,knn_pred))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8676408706629433,\n",
       " 0.8500286905627176,\n",
       " 0.8675873149458705,\n",
       " 0.8610611682797139,\n",
       " 0.8631498412455529,\n",
       " 0.8599211965877357,\n",
       " 0.8587812248957576,\n",
       " 0.8556137867717378,\n",
       " 0.8543054971118167,\n",
       " 0.8529819058184461,\n",
       " 0.8498450709613251,\n",
       " 0.8483225584331128,\n",
       " 0.8454152480777323,\n",
       " 0.8445277533376688,\n",
       " 0.8424390803718297,\n",
       " 0.8412226005126047,\n",
       " 0.8386060211927623,\n",
       " 0.8377950346199456,\n",
       " 0.8359435369725718]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "plt.plot(k_range,score_list)\n",
    "plt.xlabel(\"K value\", fontsize=12)\n",
    "plt.ylabel(\"Accuracy\", fontsize=12)\n",
    "plt.title(\"K-value vs Accuracy\")\n",
    "rcParams.update({'figure.autolayout': True})\n",
    "plt.rc('font', family='serif')\n",
    "plt.rc('xtick', labelsize=12) \n",
    "plt.rc('ytick', labelsize=12) \n",
    "plt.tight_layout()\n",
    "plt.savefig('/Users/pprusty05/google_drive/Data_Mining/Project/plots/K_accuracy_accident.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RF = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=0)\n",
    "RF.fit(X_train, y_train)\n",
    "rf_pred=neigh.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8359435369725718\n",
      "0.8415678451365031\n",
      "0.8122744712107598\n",
      "0.8730531027716458\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,rf_pred))\n",
    "print(f1_score(y_test,rf_pred))\n",
    "print(precision_score(y_test,rf_pred))\n",
    "print(recall_score(y_test,rf_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TR_Street', 'TR_City', 'TR_State', 'TR_ZipCode', 'Accident', 'Hour',\n",
       "       'WR_Severity_Heavy', 'WR_Severity_Light', 'WR_Severity_Moderate',\n",
       "       'WR_Severity_Other', 'WR_Severity_Severe', 'WR_Severity_UNK',\n",
       "       'WR_Type_Cold', 'WR_Type_Fog', 'WR_Type_Hail', 'WR_Type_Precipitation',\n",
       "       'WR_Type_Rain', 'WR_Type_Snow', 'WR_Type_Storm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final list of selected features:\n",
      "['Accident', 'WR_Type_Snow', 'WR_Type_Rain', 'TR_ZipCode', 'TR_Street', 'Hour', 'WR_Severity_Severe', 'WR_Type_Storm', 'WR_Severity_Moderate', 'WR_Type_Fog', 'WR_Severity_UNK', 'WR_Type_Precipitation', 'TR_City']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "target = 'Accident'\n",
    "#plot heatmap\n",
    "def plot_corr_matrix(df, nr_c, targ) :\n",
    "    \n",
    "    corr = df.corr()\n",
    "    corr_abs = corr.abs()\n",
    "    cols = corr_abs.nlargest(nr_c, targ)[targ].index\n",
    "    cm = np.corrcoef(df[cols].values.T)\n",
    "\n",
    "    plt.figure(figsize=(12, 12))\n",
    "    sns.set(font_scale=1.25)\n",
    "    sns.heatmap(cm, linewidths=1.5, annot=True, square=True, \n",
    "                fmt='.2f', annot_kws={'size': 10}, \n",
    "                yticklabels=cols.values, xticklabels=cols.values\n",
    "               )\n",
    "    plt.savefig('/Users/pprusty05/google_drive/Data_Mining/Project/plots/correlation.pdf')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "\n",
    "#set the threshold for feature selection\n",
    "corr_theshold=.01\n",
    "\n",
    "#find the correlation values\n",
    "corr = df_dummies.corr()\n",
    "corr_abs = corr.abs()\n",
    "\n",
    "num_cols = len(df_dummies)\n",
    "#sort the correlation values in descending order\n",
    "sort_corr = corr_abs.nlargest(num_cols, target)[target]\n",
    "#print(sort_corr)\n",
    "\n",
    "#filter the features with above correlation values\n",
    "cols_abv_corr_limit = list(sort_corr[sort_corr.values > corr_theshold].index)\n",
    "cols_bel_corr_limit = list(sort_corr[sort_corr.values <= corr_theshold].index)\n",
    "selected_feats=cols_abv_corr_limit\n",
    "nr_feats = len(cols_abv_corr_limit)\n",
    "\n",
    "#plot the heatmap with selected features\n",
    "plot_corr_matrix(df_dummies, nr_feats, target)\n",
    "\n",
    "#drop the features from df_train with correaltion value below threshold\n",
    "#for df in [df_dummies]:\n",
    "    #df.drop(cols_bel_corr_limit, inplace=True, axis=1)\n",
    "\n",
    "print(\"Final list of selected features:\")\n",
    "print(selected_feats)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(selected_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(653523, 19)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected_feat=df_dummies#drop the features from df_train with correaltion value below threshold\n",
    "cols_abv_corr_limit\n",
    "for df in [df_selected_feat]:\n",
    "    df.drop(cols_bel_corr_limit, inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(653523, 13)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected_feat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df_selected_feat['Accident']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_selected_feat.drop(['Accident'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scalar = scaler.fit_transform(X_train)\n",
    "X_test_scalar=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(X_train, y_train)\n",
    "knn_pred=neigh.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.869568876477564\n",
      "0.8731490989181064\n",
      "0.847716469449381\n",
      "0.9001549531305135\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,knn_pred))\n",
    "print(f1_score(y_test,knn_pred))\n",
    "print(precision_score(y_test,knn_pred))\n",
    "print(recall_score(y_test,knn_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "accident_rows=df_dummies[df_dummies.Accident==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_accident_rows=df_dummies[df_dummies.Accident==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## weather vs accident"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TR_Street</th>\n",
       "      <th>TR_City</th>\n",
       "      <th>TR_State</th>\n",
       "      <th>TR_ZipCode</th>\n",
       "      <th>WR_Type</th>\n",
       "      <th>WR_Severity</th>\n",
       "      <th>Accident</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5791</td>\n",
       "      <td>1670</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5791</td>\n",
       "      <td>1670</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6423</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6423</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>19696</td>\n",
       "      <td>1229</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Snow</td>\n",
       "      <td>Heavy</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>653518</td>\n",
       "      <td>11207</td>\n",
       "      <td>1591</td>\n",
       "      <td>45</td>\n",
       "      <td>1894</td>\n",
       "      <td>Fog</td>\n",
       "      <td>Severe</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>653519</td>\n",
       "      <td>11207</td>\n",
       "      <td>1591</td>\n",
       "      <td>45</td>\n",
       "      <td>1894</td>\n",
       "      <td>Fog</td>\n",
       "      <td>Severe</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>653520</td>\n",
       "      <td>11207</td>\n",
       "      <td>1591</td>\n",
       "      <td>45</td>\n",
       "      <td>1894</td>\n",
       "      <td>Fog</td>\n",
       "      <td>Severe</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>653521</td>\n",
       "      <td>11206</td>\n",
       "      <td>1591</td>\n",
       "      <td>45</td>\n",
       "      <td>1894</td>\n",
       "      <td>Fog</td>\n",
       "      <td>Severe</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>653522</td>\n",
       "      <td>14610</td>\n",
       "      <td>1271</td>\n",
       "      <td>45</td>\n",
       "      <td>1903</td>\n",
       "      <td>Fog</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>653523 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TR_Street  TR_City  TR_State  TR_ZipCode WR_Type WR_Severity  \\\n",
       "0            5791     1670        17           0    Rain       Light   \n",
       "1            5791     1670        17           0    Rain    Moderate   \n",
       "2            6423     1229        17           1    Rain       Light   \n",
       "3            6423     1229        17           1    Rain       Light   \n",
       "4           19696     1229        17           1    Snow       Heavy   \n",
       "...           ...      ...       ...         ...     ...         ...   \n",
       "653518      11207     1591        45        1894     Fog      Severe   \n",
       "653519      11207     1591        45        1894     Fog      Severe   \n",
       "653520      11207     1591        45        1894     Fog      Severe   \n",
       "653521      11206     1591        45        1894     Fog      Severe   \n",
       "653522      14610     1271        45        1903     Fog    Moderate   \n",
       "\n",
       "        Accident  Hour  \n",
       "0              0    20  \n",
       "1              0    20  \n",
       "2              0    13  \n",
       "3              0    20  \n",
       "4              0    21  \n",
       "...          ...   ...  \n",
       "653518         1    16  \n",
       "653519         1    16  \n",
       "653520         1    16  \n",
       "653521         0    15  \n",
       "653522         0     4  \n",
       "\n",
       "[653523 rows x 8 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_panda_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_sev_list = accident_panda_df.WR_Severity.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Light', 'Moderate', 'Heavy', 'Severe', 'Other', 'UNK'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_sev_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_type_list = accident_panda_df.WR_Type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_str_list = []\n",
    "accident_count_list = []\n",
    "for weather_type in weather_type_list:\n",
    "    for weather_sev in weather_sev_list:\n",
    "        accident_count, _ = accident_panda_df[(accident_panda_df.WR_Severity == weather_sev) & \n",
    "                                           (accident_panda_df.WR_Type == weather_type)].shape\n",
    "        if accident_count > 0:\n",
    "            weather_str = weather_type + ' - ' + weather_sev\n",
    "            weather_str_list.append(weather_str)\n",
    "            accident_count_list.append(accident_count)\n",
    "        #print(weather_type + ' ' + weather_sev + ' ' + str(accident_count) )\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams.update({'figure.autolayout': True})\n",
    "plt.tight_layout()\n",
    "#plt.rc('text', usetex=False)\n",
    "plt.rc('font', family='serif')\n",
    "plt.rc('xtick', labelsize=12) \n",
    "plt.rc('ytick', labelsize=12) \n",
    "plt.xticks(rotation=90)\n",
    "plt.bar(weather_str_list, accident_count_list)\n",
    "plt.xlabel('Weather condition',fontsize=12)\n",
    "plt.ylabel('Accidents',fontsize=12)\n",
    "plt.title('Weather vs Accident')\n",
    "plt.savefig('/Users/pprusty05/google_drive/Data_Mining/Project/plots/Weather_vs_Accident.pdf')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
